Conventionally, diagnosis of diseases (for example, a tumor region) has been performed by using CT (computed tomography) scanned image data in medical sites. In order to reduce the burden on a doctor when diagnosis is performed, a diagnosis support system is used. With the diagnosis support system, disease case data similar to a diagnosis target can be retrieved from a database storing previous case data and the retrieved similar case data can be displayed.
In order to create a database storing case data, it is required to extract, as case data, image data of a given region from previously taken CT image data, and determine whether the extracted case data includes tumor regions. At this time, when the number of pieces of case data is very large, the case data needs to be machine determined. Further, in order to machine determine the case data, boundary surfaces need to be calculated. The boundary surfaces are used to classify case data known to include tumor regions and case data known to not include tumor regions into respective classes.
However, as some tumor regions are similar to tissues inside the human body, it is not easy to calculate boundary surfaces suitable to determining whether case data includes tumor regions. Therefore, for boundary surfaces used to determine case data, undetermined case data that does not belong to either class is generated. In this case, a doctor determines to which class the undetermined case data belongs, and the boundary surfaces are also modified based on the class determined by the doctor. As a result, accuracy of determining whether data includes tumor regions can be enhanced.
When the boundary surfaces are modified based on the result determined by the doctor, it is desirable for the doctor to select, from the above-described undetermined case data, case data having a highly enhanced effect in terms of determination accuracy in modifying the boundary surfaces. By selecting such case data, the number of times the boundary surfaces are modified can be reduced, and thus, the modification work load can be reduced.
However, such boundary surfaces are defined in a high-dimensional feature space according to feature quantities of each case data. It is thus difficult to recognize case data having a highly enhanced effect in terms of determination accuracy.